This complete influence of an attribute now takes into consideration its importance among all the possible attribute configurations, which is closer to the original intuition behind attributes' influence. However, computing the complete influence of a single instance is extremely computationally expensive, with complexity in \(\bigcirc (2^n*l(n,x))\) , with \(n\)  the number of attributes, \(x\)  the number of instances in the dataset, and \(l(n,x)\)  the complexity of training the model to be explained.
It is then not practical to use the complete influence. Consequently, it becomes necessary to seek a more efficient way to explain predictions. Although the complete influence is too computationally heavy, it can be considered as an excellent baseline [1]}. Thus, we can evaluate other explanation methods by studying their differences with the complete influence.
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